In 2D illustration an artist makes a series of decisions throughout the process, with those choices becoming more granular over time. These decisions vary in scope and impact. Given this formulation of a creative process, I observe that there exist portions of common workflows where an artist is largely following through on the logical conclusions of prior decisions. These are the specific areas of these creative workflows where I believe that co-creative tools can be implemented while retaining creative control over the completed artifact. My work so far has been in implementing such tools for steps in illustration and frame-by-frame animation. I intend to design further process-centered co-creative systems and study their usability and impact in the context of existing workflows.
Narrative planning can be used to create structured interactive experiences that dynamically respond to user input. Narrative planning works by generating a sequence of actions that achieves an author's desired goal while ensuring that there is an explanation for why each character takes each action in the sequence. An action in a sequence is considered necessary to that sequence if leaving the action out would prevent a later action in the sequence from being taken or prevent an author or character goal from being achieved. Using this definition, we define the causal width of a sequence to be the number of causally unnecessary actions, and we hypothesize sequences with a lower causal width are more likely to lead to a solution. We show that using causal width as a ranking mechanism can sometimes improve blind search, and ignoring stories with a high causal width can always improve the performance of heuristic search on a set of story benchmark problems.
We formalize the sensemaking needed to play information storygames as a task grounded in automated plan recognition, and outline extensions to a baseline model thereof needed to account for a key part of the information storygame-play loop: non-linear discovery of ambiguous information. This novel problem setting is non-trivial—mechanically simulating this narrative sensemaking requires piecing together incompletely-specified events to reason about the means through which particular ends were achieved in the virtual world. Our work readies plan recognition systems for the task by extending a foundational compilation of plan recognition as planning to cover partially-ordered and lifted observations of both actions and facts. While state-of-the-art plan recognizers approximate piecemeal aspects of these features, they do so in isolation of each other and by appealing to disparate and complex frameworks. In contrast, we achieve these functions within a classical planning-based framework. Our results confirm that, while slower, our approach never has more (and often has fewer) false positives than the baseline model in predicting the ground truth plan being executed. We discuss our findings in the context of future work toward better simulating human information storygame sensemaking.
Many recent advances in artificial intelligence remain difficult to apply in real games due to their black-box nature. These systems often lack transparency and control, making them difficult to integrate into games where player experience and narrative coherence are important. Unpredictable or inexplicable agent behavior can confuse players and frustrate developers. My dissertation explores how program synthesis can address this issue by generating interpretable, controllable representations of agent behavior. Instead of relying on black-box neural network policies, symbolic programs are extracted or generated that capture agent logic in a readable and editable form. Several methods are explored: synthesizing functional programs to imitate and explain game agents, adapting logical program policies to multi-agent settings, and evaluating large language models for code generation across domains such as simplified Atari games, Baba is You, and also tabletop games. An open research question is whether the created conceptual program libraries are transferable across different game domains. My research aims to bridge the gap between AI capabilities and game development needs by making agent behavior transparent, explainable, and adaptable for developers.
Narrative generation frameworks often face a trade-off between character believability and storyline adherence. Planner-based approaches ensure adherence to authorial designs at the cost of character agency and believability. In contrast, emergent narratives excel at presenting believable characters but often lack meaningful plot progression. We present DiriGent, a novel cognitive framework for agent modeling and narrative generation that enables authentic behavior while maintaining storyline adherence. Our agents possess a dynamic belief system and role-based ideal worlds that encode their basic values and relationships. We leverage Large Language Models (LLMs) to analyze the tensions between an agent's ideal worlds and their perceived actual world, which motivates their belief-driven actions. The system then adjusts the narrative world to amplify these tensions, thereby steering agent behavior toward the desired storyline. This dynamic framework allows agents to evolve meaningfully as the story unfolds, overcoming limitations of static agent profiles such as OCEAN. We evaluate our approach by generating stories for five story prompts. Our evaluation, consisting of automated LLM judges and human assessments, demonstrated significant improvements in character development and character motivation compared to baselines, while preserving storyline adherence. Our work presents a path toward interactive narratives that deliver rich characters and enable unique user experiences while adhering to desired storylines.
